Position Types

Interships

Paid Internship on Acceleration of HPC/BigData Algorithms for Behavioral Experiments

Topic

Tracking of mouth whiskers in many mammals is characteristic of their brain activity, similar to what finger movement is in humans. Neuroscientists can deduce a plethora of information on behavior by mounting whisker-tracking experiments, i.e. experiments where animals (typically, mice, rats) are being tracked for their whisker movements subject to various stimuli such as air puff in their eyes, auditory stimuli, and so on. In the Erasmus MC we have developed an experimental setup which records whisker movements on head-fixed mice. Recording is done through a high-speed camera that generates large amounts of image stacks which are then sent to a computer for post-processing through a powerful yet slow Matlab program (http://bwtt.sourceforge.net/docs/). Current experiment runs generate 15 seconds of whisker-tracking video which occupies 2-4 GB of disk space to store and takes about 2 weeks of post-processing in Matlab. At the moment, dozens of videos are generated per week, which puts high pressure not only on the storage equipment needed but is also detrimental to the fast and efficient analysis of the behavioral experiments.

The goal is to study the open-source Matlab code and port the compute- and data-intensive parts of it to a high-performance, FPGA-based computing platform (Maxeler). This not only will accommodate experiments in the lab but will also be the first, crucial step for supporting closed-loop behavioral experiments, where specific whisker movements will evoke (in real time) a suitable response by the analysis machine, leveraging a crucial class of neuroscientifically relevant experiments.

Internship details

A student internship is offered on the topic (salary depending on candidate expertise) for a minimum duration of 6 months and a maximum of 12 months. Productivity bonuses are possible.

Paid Internship on System for ultra-fast, two-way cognitive brain-machine interface

Topic

The activity of brain neural networks determines our cognitive processes. With modern methods, we can monitor the electrical activity of up to hundreds neurons, and decode its information content. In this way we are beginning to understand for example how memory works. At the same time, we now have a way of perturbing neuronal activity by using light. If we could detect complex neural patterns, decode them in real-time and disrupt them selectively, we would have a tool for “editing” memory and cognition and of understanding their mechanisms.

In this project, we plan to extend an existing open-source, multi-channel, data-acquisition system to perform low-latency, online data analysis on a FPGA module and drive an array of stimulators. The intended system would be at the cutting edge of current neurotechnology, and may help to produce significant advances in brain research.

See above figure , for the overall system block diagram. Currently, closed-loop control is implemented at block (4) which is custom software running on a PC. Closing the loop this way is obviously very slow, typically taking up 10s to 100s of milliseconds – which is way to slow for real-time closed-loop control. We essentially want to migrate the algorithms running in block (4) (i.e. the PC) to the Xilinx Spartan FPGA residing in block (3) and effectively drive response times down to a few milliseconds or less. The FPGA already resides on a suitable dual-PCB system and all needed pinouts (analog and digital I/O) are working.

Internship details

A student internship is offered on the topic (salary depending on candidate expertise) for a minimum duration of 6 months and a maximum of 12 months. Productivity bonuses are possible.

Note: This internship has also been posted on the HiPEAC website for the PhD internships 2016 (Link).